The present invention relates to data communication and, in particular embodiments, to the communication of data over channel exhibiting intersymbol interference.
An intersymbol interference (ISI) channel is one in which the signal energy of a signal point transmitted in one signaling interval becomes dispersed over a number of adjacent signaling intervals. One example is a channel used to communicate data over a cable TV coaxial cable. Another is a wireless, or cellular, telecommunications channel, the dispersion being principally due to the phenomenon referred to as multi-path. In any such ISI channel, the dispersed energy combines with signal points transmitted in the adjacent intervals and thus constitutes a source of noise in those other interval . When the level of ISI is small, a so-called linear equalizer is effective in mitigating against it. However, if the ISI is severe, other, more powerful techniques must be brought into play. Typically, a decision feedback equalizer (DFE) is used. DFE is an interference cancellation technique. It estimates the amount of ISI in a given received signal point and subtracts the ISI estimate therefrom to arrive as a ISI-compensated signal point from which a decision is made as to the identity of the transmitted signal point.
Even better results can be achieved using a technique referred to as "maximum likelihood decoding for ISI channels." (See, for example, G. D. Forney, Jr., "The Viterbi Algorithm," Proc. IEEE, Vol. 61, pp. 268-278, March 1973.). That technique takes advantage of the recognition that the ISI phenomenon can be modeled as a for of convolutional coding within the channel. Therefore known techniques for decoding convolutional codes, such as Viterbi decoding, can be applied to the received ISI-corrupted signal even in a case where the transmitted signal point stream was not processed with any explicit convolutional coding in the transmitter. The underlying theory of this approach is that it provides what I have come to refer to as "conversion gain," this being the improvement in error immunity that results from the con version of at least a portion of the harmful ISI into useful signal energy. Thus rather than Subtracting the ISI energy from the received signal, the ISI is returned to the signaling interval from which it came. The signal-to-noise ratio, and therefore the receiver error performance, are thereby improved.
Practical application of this approach is quite limited, however. The number of so-called states, S, in the Viterbi decoder is roughly given by S=C.sup.L, where C is the number of signal points in the transmitter constellation (its "size"), and L is the number of signaling intervals over which there is significant dispersion. Thus except for cases in which C and L are relatively small, the number of states, S, and thus the associated complexity of the Viterbi decoder, will be prohibitively large from an implementational standpoint. Indeed, few present-day communication systems meet the criterion of small C and/or small L. Moreover, if explicit convolutional coding were to be implemented in the transmitter, the complexity would be even far greater, reducing even further the practical applicability of this technique. The principal object the invention, then, is to be able to achieve the performance advantage offered by the above-described maximum likelihood decoding for ISI channels, without suffering the implementational complexity that arises for large values of C and/or L.